Econometrica: Nov, 2018, Volume 86, Issue 6
Identifying Effects of Multivalued Treatments
https://doi.org/10.3982/ECTA14269
p. 1939-1963
Sokbae Lee, Bernard Salanié
Multivalued treatment models have typically been studied under restrictive assumptions: ordered choice, and more recently, unordered monotonicity. We show how treatment effects can be identified in a more general class of models that allows for multidimensional unobserved heterogeneity. Our results rely on two main assumptions: treatment assignment must be a measurable function of threshold‐crossing rules, and enough continuous instruments must be available. We illustrate our approach for several classes of models.
Supplemental Material
Supplement to "Identifying Effects of Multivalued Treatments"
Appendix A gives an identification result for the zero-index case, which was not dealt with in the text. It also provides a characterization of Heckman and Pinto's unordered monotonicity property as a subcase of our more general framework. Appendix B collects proofs of some of the results in the main text. Finally, Appendix C fills in the details of the entry game introduced in Section 2, and Appendix D compares our results with those of Heckman, Urzua, and Vytlacil (2008) in more detail. Appendix E discusses a more general form of threshold conditions than the "rectangular"
threshold conditions in Assumption 2.1.
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